Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome.

Background: Head and neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor-node-metastasis staging system does not satisfy the requirements for screening treatment-sensitive patients. Thus, an ideal biomarker leading to precise screening and treatment of HNSCC is urgently needed. Methods: Ten machine learning algorithms-Lasso, Ridge, stepwise Cox, CoxBoost, elastic network (Enet), partial least squares regression for Cox (plsRcox), random survival forest (RSF), generalized boosted regression modelling (GBM), supervised principal components (SuperPC), and survival support vector machine (survival-SVM)-as well as 85 algorithm combinations were applied to construct and identify a consensus immune-derived gene signature (CIDGS). Results: Based on the expression profiles of three cohorts comprising 719 patients with HNSCC, we identified 236 consensus prognostic genes, which were then filtered into a CIDGS, using the 10 machine learning algorithms and 85 algorithm combinations. The results of a study involving a training cohort, two testing cohorts, and a meta-cohort consistently demonstrated that CIDGS was capable of accurately predicting prognoses for HNSCC. Incorporation of several core clinical features and 51 previously reported signatures, enhanced the predictive capacity of the CIDGS to a level which was markedly superior to that of other signatures. Notably, patients with low CIDGS displayed fewer genomic alterations and higher immune cell infiltrate levels, as well as increased sensitivity to immunotherapy and other therapeutic agents, in addition to receiving better prognoses. The survival times of HNSCC patients with high CIDGS, in particular, were shorter. Moreover, CIDGS enabled accurate stratification of the response to immunotherapy and prognoses for bladder cancer. Niclosamide and ruxolitinib showed potential as therapeutic agents in HNSCC patients with high CIDGS. Conclusion: CIDGS may be used for stratifying risks as well as for predicting the outcome of patients with HNSCC in a clinical setting.

Selection of reference genes in liproxstatin-1-treated K562 Leukemia cells via RT-qPCR and RNA sequencing.

BACKGROUND: Reverse transcription quantitative polymerase chain reaction (RT-qPCR) can accurately detect relative gene expression levels in biological samples. However, widely used reference genes exhibit unstable expression under certain conditions. METHODS AND RESULTS: Here, we compared the expression stability of eight reference genes (RPLP0, RPS18, RPL13, EEF1A1, ß-actin, GAPDH, HPRT1, and TUBB) commonly used in liproxstatin-1 (Lip-1)-treated K562 cells using RNA-sequencing and RT-qPCR. The expression of EEF1A1, ACTB, GAPDH, HPRT1, and TUBB was considerably lower in cells treated with 20 µM Lip-1 than in the control, and GAPDH also showed significant downregulation in the 10 µM Lip-1 group. Meanwhile, when we used geNorm, NormFinder, and BestKeeper to compare expression stability, we found that GAPDH and HPRT1 were the most unstable reference genes among all those tested. Stability analysis yielded very similar results when geNorm or BestKeeper was used but not when NormFinder was used. Specifically, geNorm and BestKeeper identified RPL13 and RPLP0 as the most stable genes under 20 µM Lip-1 treatment, whereas RPL13, EEF1A1, and TUBB were the most stable under 10 µM Lip-1 treatment. TUBB and EEF1A1 were the most stable genes in both treatment groups according to the results obtained using NormFinder. An assumed most stable gene was incorporated into each software to validate the accuracy. The results suggest that NormFinder is not an appropriate algorithm for this study. CONCLUSIONS: Stable reference genes were recognized using geNorm and BestKeeper but not NormFinder. Overall, RPL13 and RPLP0 were the most stable reference genes under 20 µM Lip-1 treatment, whereas RPL13, EEF1A1, and TUBB were the most stable genes under 10 µM Lip-1 treatment.

[IL-6 Regulates the Chemosensitivity of Drug-Resistant Multiple Myeloma Cell Lines to Bortezomib through STAT3/Notch Signaling Pathway].

OBJECTIVE: To investigate the effect of interleukin-6 (IL-6) on the chemosensitivity of drug-resistant multiple myeloma (MM) cell lines to bortezomib (BTZ) and its mechanism. METHODS: Peripheral blood samples were collected from patients with BTZ-resistant MM before and after treatment. Human MM cell lines KM3 and KM3/BTZ were cultured in vitro. ELISA was used to detect the content of IL-6 in peripheral blood of MM patients, KM3 and KM3/BTZ cells. CCK-8 assay was used to detect the drug sensitivity of KM3 and KM3 / BTZ cells to BTZ. KM3 / BTZ cells were divided into KM3/BTZ control group (normal culture for 48 h), IL-6 neutralizing antibody Anti-IL-6 group (500 ng/ml Anti-IL-6 treated for 48 h), BTZ group (300 ng/ml BTZ treated for 48 h), BTZ + Anti-IL-6 group (300 ng/ml BTZ and 500 ng/ml Anti-IL-6 treated for 48 h). The proliferation activity of KM3 / BTZ cells was detected by CCK-8 assay. The cell cycle distribution of KM3/BTZ cells was detected by flow cytometry. The apoptosis of KM3/BTZ cells was detected by Annexin V-FITC/PI double staining. The mRNA expression levels of IL-6, Notch1, signal transducer and activator of transcription 3 (STAT3) in KM3/BTZ cells were detected by real-time fluorescent quantitative PCR (qRT-PCR), and the protein expression levels of IL-6, Notch1, STAT3 in KM3/BTZ cells were detected by Western blot. RESULTS: The level of IL-6 in peripheral blood of patients with BTZ-resistant MM after treatment was significantly higher than that before treatment (P<0.05). The level of IL-6 in KM3/BTZ cells was significantly higher than that in KM3 cells (P<0.05). The sensitivity of KM3/BTZ cells to BTZ was significantly lower than that of KM3 cells (P<0.05), and the resistance index (RI) was 19.62. Anti-IL-6 and BTZ could inhibit the proliferation of KM3 / BTZ cells, block cell cycle, and induce apoptosis (P<0.05). Compared with single drug treatment, the combined effect of Anti-IL-6 and BTZ was more obvious on KM3/BTZ cells (P<0.05), and significantly down regulated the mRNA and protein expression of IL-6, Notch1 and STAT3 in KM3/BTZ cells (P<0.05). CONCLUSION: Antagonizing IL-6 can increase the chemosensitivity of MM cells to BTZ, and IL-6 may reduce the sensitivity of MM cells to BTZ through STAT3/Notch signaling pathway.

Liproxstatin­1 induces cell cycle arrest, apoptosis, and caspase­3/GSDME­dependent secondary pyroptosis in K562 cells.

Leukemia is a fatal hematopoietic disorder with a poor prognosis. Drug resistance is inevitable after the long­term use of chemotherapeutic agents. Liproxstatin­1, commonly known as a ferroptosis inhibitor, has never been reported to have anticancer effects. In the present study, the antileukemic role of liproxstatin­1 in K562 leukemia cells was investigated. Liproxstatin­1 inhibited K562 cell proliferation in a dose­ and time­dependent manner. RNA sequencing revealed several pathways that were affected by liproxstatin­1, such as the G1/S transition of the mitotic cell cycle and extrinsic or intrinsic apoptotic signaling pathways. The results of flow cytometry indicated that liproxstatin­1 arrests the cell cycle at the G1 phase, and even at the G2/M phase. p21WAF1/CIP1, a cyclin­dependent kinase inhibitor, was upregulated. It was also determined that liproxstatin­1 induced BAX and TNF­α expression, which was accompanied by cleavage of caspase­3 and PARP. The caspase­3­specific inhibitor z­DEVD­FMK rescued some of the apoptotic cells. Interestingly, K562 cells were characterized by swelling and plasma membrane rupture when treated with a high concentration of liproxstatin­1, which was inconsistent with the typical apoptotic appearance. Thus, it was hypothesized that apoptosis­mediated pyroptosis occurs during liproxstatin­1­induced cell death. The expression of the hallmark of pyroptosis, the cleaved N­terminal GSDME, increased. Additionally, it was observed that endoplasmic reticulum stress and autophagy were involved in liproxstatin­1­induced cell death. Collectively, liproxstatin­1 induced cell cycle arrest, apoptosis, and caspase­3/GSDME­dependent secondary pyroptosis in K562 leukemia cells, which provides new hope for the treatment of leukemia.